Modifying an unreliable training set for supervised classification (US6298351)

Patent	Pub.Date	Inventor	Assignee	Title
US4719571	1988-01	Rissanen et al.	International Business Machines Corporation	Algorithm for constructing tree structured classifiers
US5136686	1992-08	Koza		Non-linear genetic algorithms for solving problems by finding a fit composition of functions
US5335291	1994-08	Kramer et al.	Massachusetts Institute of Technology	Method and apparatus for pattern mapping system with self-reliability check
US5481649	1996-01	Birdwell et al.	The University of Tennessee Research Corp.	Method and apparatus using a decision tree in an adjunct system cooperating with another physical system
US5555317	1996-09	Anderson	Eastman Kodak Company	Supervised training augmented polynomial method and apparatus for character recognition
US5559929	1996-09	Wasserman	Unisys Corporation	Method of enhancing the selection of a training set for use in training of a neural network
US5634087	1997-05	Mammone et al.	Rutgers University	Rapidly trainable neural tree network
US5684929	1997-11	Cortes et al.	Lucent Technologies Inc.	Method and apparatus for determining the limit on learning machine accuracy imposed by data quality
US5687364	1997-11	Saund et al.	Xerox Corporation	Method for learning to infer the topical content of documents based upon their lexical content
US5727081	1998-03	Burges et al.	Lucent Technologies Inc.	System and method for automated interpretation of input expressions using novel a posteriori probability measures and optimally trained information processing networks
US5727199	1998-03	Chen et al.	International Business Machines Corporation	Database mining using multi-predicate classifiers
US5734893	1998-03	Li et al.	IBM Corporation	Progressive content-based retrieval of image and video with adaptive and iterative refinement
US5768422	1998-06	Yaeger	Apple Computer, Inc.	Method for training an adaptive statistical classifier to discriminate against inproper patterns
US5799301	1998-08	Castelli et al.	International Business Machines Corporation	Apparatus and method for performing adaptive similarity searching in a sequence database
US5870399	1999-02	Smith	National Engineering & Assessment Group Ltd.	Telecommunications system comprising a communications protocol analyzer which comprises a neural network that obviates the need for a database
US5956739	1999-09	Golding et al.	Mitsubishi Electric Information Technology Center America, Inc.	System for text correction adaptive to the text being corrected
US6067535	2000-05	Hobson et al.	Notel Networks Corporation	Monitoring and retraining neural network
US6111983	2000-08	Fenster et al.	The Trustees of Columbia University in the City of New York	Determination of image shapes using training and sectoring


	Foreign References:			None
	Other References:			Castelli et al., "The Relative Value of Labeled and Unlabled Samples in Pattern Recognition with an Unknown Mixing Parameter," International Symposium on Information Theory, Norway, p. 2103-2117, Dec. 1999.* Castelli et al., "The Relative Value of Labeled and Unlabled Samples in Pattern Recognition," International Symposium on Information Theory, IEEE, p. 355-355, 1993.* Castelli et al., "Classification Rules in the Unknown Mixture Parameter Case; Relative Value of Labeled and Unlabled Samples," International Symposium on Information Theory, IEEE, p. 111, 1994.* Castelli et al., "Classification Rules in the Unknown Mixture Parameter Case Relative Value of Labeled and Unlabeled Samples," Information Theory, Jun. 1994, Proceedings of IEEE, p. 111.* Pinciroli et al., "A Technological Environment and a Software Product for Teaching Dynamic Electrocardiography," Computers in Cardiography, 1988, Proceedings, pp. 473-476.* Li et al., "HierarchyScan: A Heirarchical Similarity Search Algorithm Databases of Long Sequences," Data Engineering, Proceedings, p. 546-553, Feb. 1996.* Castelli et al., "Progressive Classification in the Compressed Domain for EOS Satellite Databases," Acoustics, Speech, and Signal Processing, IEEE, pp. 2199-2202, vol. 2, May 1996.* Duda et al., Pattern Classification and Scene Analysis, "Bayes Decision Theory" (Chapter 2), pp. 10-13, Wiley & Sons (1973). Duda et al., Pattern Classification and Scene Analysis, "Parameter Estimation And Supervised Learning", (Chapter 3), pp. 44-45, 76-79, Wiley & Sons (1973). Duda et al., Pattern Classification and Scene Analysis, "Unsupervised Learning And Clustering", (Chapter 6), p. 189-191, Wiley & Sons (1973). "Progressive Classification In The Compressed Domain For Large EOS Satellite Databases", by Vittorio Castelli, Chung-Sheng Li, John Turek, Ioannis Knotoyiannis, IEEE 1996, p. 104.
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